Sewage inflow amount predicting device and method, and server device

ABSTRACT

A case base ( 54 ) is generated from historical data containing inflow data ( 31 ) that indicates a sewage inflow measured at a sewage treatment plant ( 10 ) and meteorological data ( 33 ) corresponding to the inflow data ( 31 ). A similar case search section ( 56 ) and output estimation section ( 57 ) predict a sewage inflow corresponding to an input prediction condition ( 40 ) after a predetermined prediction time in real time using the case base ( 54 ) and output inflow prediction data ( 20 ).

TECHNICAL FIELD

The present invention relates to a sewage inflow prediction apparatusand method of predicting a sewage inflow to sewage treatment facilities,and a server apparatus.

A sewage treatment plant purifies incoming sewage by microbiologicaltreatment. Normally, since microbiological treatment very slowlyprogresses, it must efficiently be done on the basis of the inflow.Hence, it is very important for the sewage treatment plant to accuratelypredict the sewage inflow. Conventionally, to predict the sewage inflow,formulas using physical laws are created on the basis of rough values ofcity design information, e.g., the sewage pipe network structure,topography, and population in the region around the sewage plant. Thesewage inflow is predicted using the formulas.

Since the conventional sewage inflow prediction technique uses formulasbased on the physical laws, a number of factors are necessary forobtaining an accuracy. However, factors usable in formulas are limitedas a matter of course. In addition, since rough city design informationis used, the sewage inflow cannot be accurately predicted. For example,sewage includes house drainage, industrial wastewater from plants, andrainwater. The sewage inflow varies due to the influence of many factorssuch as rainfall, specific days (holidays), and seasonal changes as wellas social changes (e.g., an increase/decrease in population or thenumber of plants) around the sewage plant. Furthermore, since the sewagepipe network in a city is very complex, these factors can hardly beaccurately expressed by formulas using physical laws.

DISCLOSURE OF INVENTION

The present invention has been made to solve the above problems, and hasas its object to provide a sewage inflow prediction apparatus and methodcapable of accurately predicting a sewage inflow to a sewage treatmentplant, and a server apparatus.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a sewage inflow prediction apparatusaccording to an embodiment of the present invention;

FIG. 2 is a block diagram showing a sewage inflow prediction systemaccording to an embodiment of the present invention;

FIG. 3 is a sequence chart showing the operation of the sewage inflowprediction system;

FIG. 4 is an explanatory view showing the concept of phases used in acase-based reasoning model;

FIGS. 5A, 5B, 5C, and 5D are explanatory views showing quantizationprocessing of an input space;

FIG. 6 is a flow chart showing case base generation processing;

FIG. 7 is a flow chart showing input quantization number determinationprocessing;

FIG. 8 is an explanatory view showing an output distribution condition;

FIG. 9 is an explanatory view showing a continuity condition;

FIG. 10 is an explanatory view showing a case compression condition;

FIGS. 11A, 11B, 11C, and 11D are explanatory views showing casegeneration processing;

FIG. 12 is a flow chart showing case generation processing;

FIG. 13 is an explanatory view showing the definition of similarity;

FIG. 14 is a flow chart showing similar case search processing;

FIGS. 15A and 15B are explanatory views showing output estimationoperation (when a similar case is present);

FIGS. 16A and 16B are explanatory views showing output estimationoperation (when no similar case is present);

FIGS. 17A and 17B are explanatory views showing adaptive learningoperation (when a corresponding case is present);

FIGS. 18A and 18B are explanatory views showing adaptive learningoperation (when no corresponding case is present); and

FIG. 19 is a view showing a simulation result obtained when a case-basedreasoning model is used.

BEST MODE FOR CARRYING OUT THE INVENTION

[Arrangement of Sewage Inflow Prediction Apparatus]

The embodiment of the present invention will be described next withreference to the accompanying drawings.

FIG. 1 is a block diagram showing a sewage inflow prediction apparatusaccording to an embodiment of the present invention. An example whereinan incoming sewage inflow is predicted in a sewage treatment plant whichpurifies sewage including house drainage, industrial wastewater fromplants, and rainwater by microbiological treatment and discharges thepurified water to rivers will be described. The present invention can beapplied not only to a sewage treatment plant but also to predict asewage inflow to sewage treatment facilities that receive sewage, e.g.,pump facilities provided upstream a sewage treatment plant.

A sewage treatment plant 10 has facilities including a settling basinwhere sediments are removed from wastewater flowing from soil pipes(sewage pipes), a first sedimentation basin where suspended matterscontained in the wastewater from the settling basin sediment overseveral hrs, an aeration tank where activated sludge containing microbesis added to the supernatant liquid obtained in the first sedimentationbasin, and aerated to perform microbiological treatment, a finalsedimentation basin where the activated sludge is converted into aspongy form by the treatment in the aeration tank sediments over severalhrs, and a sterilization basin where a sterilization liquid such assodium hypochlorite is injected into the supernatant liquid obtained inthe final sedimentation basin to sterilize it. Water treated in thesterilization basin is discharged to a river or sea by a pump.

A sewage inflow prediction apparatus 50 generates a case base on thebasis of historical data 30 obtained from the past records, predicts theinflow of sewage that should be received after a predetermined time onthe basis of a prediction condition 40 that specifies the predictedinflow, and outputs inflow prediction data 20. In the sewage treatmentplant 10, sewage purification treatment is controlled on the basis ofthe inflow prediction data 20. Note that a black box prediction model isa prediction model which is extracted using a plurality of historicaldata formed from combinations of input and output values and indicatesthe input/output relationship between the objects. Hence, the formulasthat indicate the input/output relationship between the objects need notbe derived using physical laws.

The sewage inflow prediction apparatus 50 is constituted by an inputsection 51 which receives the historical data 30 and predictioncondition 40, a number of historical data 52 formed from the receivedhistorical data 30, a case base generation section 53 which generates acase base 54 containing a number of cases using the historical data 52,and a similar case search section 56 which searches the case base 54 fora similar case on the basis of newly input prediction condition 40. Thesewage inflow prediction apparatus 50 also has an output estimationsection 57 which estimates, from at least one similar case detected bythe similar case search section 56, an inflow corresponding to the newprediction condition 40 and outputs the inflow prediction data 20 after,e.g., 60 to 120 min, 24 hrs, etc., and an adaptive learning section 55which partially revises the case base 54 on the basis of the newhistorical data 30. Of these components, the case base generationsection 53, similar case search section 56, output estimation section57, and adaptive learning section 55 are implemented by software.

[Operation of Sewage Inflow Prediction Apparatus]

The operation of the sewage inflow prediction apparatus 50 according tothis embodiment will be described next with reference to FIG. 1. Thesewage inflow prediction apparatus 50 receives the historical data 30through the input section 51 before inflow prediction and stores thedata as the historical data 52. The case base 54 is generated using thehistorical data 52. As the historical data 30, inflow data 31, time data32, and meteorological data 33 are used. The inflow data 31 indicatesthe actual inflow such as the 1-hr time inflow or 1-day total inflowmeasured by the flowmeter in the sewage treatment plant 10.

The time data 32 indicates the time, type of day, and season when theinflow data 31 is obtained. The meteorological data 33 indicates the airtemperature, rainfall, and weather at the sewage treatment plant 10 orin the objective region. Of the historical data 30, data measured at thesewage treatment plant 10 are used as the inflow data 31 and time data32. As for the meteorological data 33, data measured at the sewagetreatment plant 10 or in the objective region may be used.Alternatively, past meteorological data may be acquired from ameteorological data provider.

The similar case search section 56 searches for a similar case on thebasis of the prediction condition 40 input from the input section 51using the case base 54 generated on the basis of the historical data 52.The output estimation section 57 estimates an inflow corresponding tothe prediction condition 40 from at least one similar case detected bythe similar case search section 56 and outputs the inflow predictiondata 20 after, e.g., 60 to 120 min. The sewage treatment plant 10prevents an increase in sewage inflow on the basis of the inflowprediction data 20.

The prediction condition 40 is a variable that specifies the inflow tobe predicted by the sewage inflow prediction apparatus 50 and containsan inflow parameter 41, time parameter 42, and meteorological parameter43. Of these parameters, the inflow parameter 41 corresponds to theinflow data 31. The inflow parameter 41 is formed from record data thatindicates the actual inflow such as a time-series inflow or a 1-daytotal inflow measured by the flowmeter in the sewage treatment plant 10.Recorded data in the past time period of several hours retroactive fromthe time position (e.g., after 90 min) of the inflow to be predicted isused.

The time parameter 42 corresponds to the time data 32. Pieces ofinformation about time, including day type information representing thetype of a day, e.g., a weekday, holiday, day before a holiday, nationalholiday, the end/beginning of the year, operation day, and seasoninformation that indicates the season to which the day belongs, are usedindependently or in combination. For example, to predict the inflow atnoon tomorrow, “noon” is set as time information. If tomorrow is Sunday,“holiday” is set as day type information.

The meteorological parameter 43 corresponds to the meteorological data33. Information about environments, including air temperatureinformation, weather information, and rainfall information at the timeposition of the inflow to be predicted, are used independently or incombination. For example, to predict the inflow at noon tomorrow, airtemperature at the same time of yesterday, i.e., yesterday noon orpredicted air temperature at noon tomorrow may be set. As weatherinformation, predicted weather of tomorrow may be set. As the rainfallinformation, predicted rainfall of tomorrow may be set.

As the meteorological parameter, a desired parameter is acquired fromthe provider who provides meteorological data. For example, rainfall isprovided every 1 hr from the Meteorological Agency or every 10 min froma river information center. As weather, short-term prediction by theMeteorological Agency or a weather predicted value obtained by ameteorological radar is used. As air temperature, short-term predictionby the Meteorological Agency, data from the AMeDAS for the objectivedistrict of the sewage treatment plant, or data measured by athermometer actually installed in the district is used.

The pieces of individual information used in the historical data 30 orprediction condition 40 are not limited to the above-describedinformation and can appropriately be changed in accordance with thesystem configuration or required prediction accuracy. For example, aninflow related to the inflow to be predicted, e.g., information about aninflow such as the inflow 24 hrs before the inflow to be predicted orthe total inflow of the previous day may be combined. As such inflowinformation, data received in the past can be used.

Actually, to predict the sewage inflow at time T1, i.e., predeterminedtime after prediction time T0, the inflow parameter 41 measured beforethe time T0, the time parameter 42 at the time T1, and the predictedcase base generation section 53 at the time T1 are used as theprediction condition 40. Hence, for the historical data 30 to be used togenerate the case base 54, time matching/time-series processing isperformed whereby inflow data (first inflow data) corresponding to thequestion of a case experienced in the past (measured before the time T0)and inflow data (second inflow data) corresponding to the answer(measured at the time T1) are generated.

The adaptive learning section 55 individually revises each case of thecase base 54 on the basis of the historical data 30 containing thesewage inflow newly measured at the sewage treatment facility. In thiscircumstance as well, time matching/time-series processing is performedwhereby inflow data (fourth inflow data) corresponding to the questionof a case experienced in the past (measured before the time T0) andinflow data (third inflow data) corresponding to the answer (measured atthe time T1) are generated. As the time data 32 or meteorological data33, data corresponding to the inflow data as the answer is used.

Of the case base 54, the output values of cases corresponding to thehistorical data 30 are changed at a predetermined ratio in accordancewith the output values of the historical data 30. If no correspondingcase is present in the historical data 30, a new case is added to thehistorical data 30.

In this way, a prediction model is generated from the historical dataactually measured at the sewage treatment plant, and sewage inflowprediction data corresponding to a desired prediction condition isestimated using the prediction model. This ensures a practicaloperability. A future inflow can sufficiently reliably be predicted inconsideration of the time required for sewage purification treatment.Hence, as compared to the conventional system, which executes predictionusing formulas based on physical laws, the inflow can be predicted usinga relatively small number of variables. Rough city design informationneed not be used.

Especially, when the sewage treatment plant cannot do full treatmentbecause of an abrupt increase in sewage inflow due to, e.g., rainfall,emergency measures must be taken to make a detour of the sewage toanother sewage treatment plant or inject a disinfectant such as chlorineto the sewage and discharge it to a river. To do this, operators must bedispatched to a pump facility or disinfectant injection facility distantfrom the sewage treatment plant. Hence, the necessity of the measuresmust be determined at an early stage (e.g., 60 min before actuallytaking such measures). According to the above embodiment, the necessityof the measures can precisely be determined on the basis of theaccurately predicted inflow. Even in the above circumstance, appropriatemeasures can be taken.

An example wherein the case base is used as a black box prediction modelhas been described above. However, the present invention is not limitedto this. For example, a prediction model using a fuzzy reasoning modelor neural network may be used. Especially, according to experimentsconducted by the present inventors using the case base, the inflow canbe predicted at a sufficient accuracy for operation only by using timeas the time parameter, air temperature and rainfall as themeteorological parameters, and using these parameters and the actualsewage inflow value, so the sewage inflow can be predicted at apractical accuracy.

[Configuration of Sewage Inflow Prediction Data Broadcast System]

A sewage inflow prediction data broadcast system according to thepresent invention will be described next with reference to FIG. 2. Thissewage inflow prediction data broadcast system comprises a plurality ofsewage treatment plants 6A to 6N, prediction data broadcast server 7,meteorological data providing center 8, and communication network 9.

Each of the sewage treatment plants 6A to 6N has a Web terminal formedfrom a computer. The sewage treatment plants 6A to 6N access theprediction data broadcast server 7 through the communication network 9such as the Internet. The prediction data broadcast server 7 predictsthe sewage inflow for each of the sewage treatment plants 6A to 6N usinga predetermined black box prediction model and broadcasts the sewageinflow to the sewage treatment plants 6A to 6N through the communicationnetwork 9 such as the Internet.

The prediction data broadcast server 7 has a management section 71,model generation section 72, inflow prediction section 73, and databroadcast section 74.

The management section 71 manages the entire prediction data broadcastservice. At the time of data broadcast contract, the management section71 issues a user ID and password for each of the registrants, i.e.,sewage treatment plants 6A to 6N. The management section 71 also managesregistrant information including the position information of the sewagetreatment plants 6A to 6N and their contact addresses. The contract maybe made through the communication network 9 or offline by mail or thelike.

The model generation section 72 generates/updates the prediction modelfor each of the sewage treatment plants 6A to 6N before broadcastingprediction data. For example, the case base generation section 53 of thesewage inflow prediction apparatus 50 shown in FIG. 1 is prepared suchthat the black box prediction model, i.e., the case base 54 for each ofthe sewage treatment plants 6A to 6N is individually generated from thehistorical data 30 containing the inflow data 31 obtained at each of thesewage treatment plants 6A to 6N.

Even after the adaptive learning section 55 is arranged, and theprediction data broadcast service is started, the prediction model maybe updated from the historical data obtained at each sewage treatmentplant.

The inflow prediction section 73 individually predicts the sewage inflowusing the prediction model for each of the sewage treatment plants 6A to6N, which is generated by the model generation section 72. For example,the similar case search section 56 and output estimation section 57 ofthe sewage inflow prediction apparatus 50 shown in FIG. 1 and the casebase 54 for each of the sewage treatment plants 6A to 6N are arranged,and the sewage inflow for each of the sewage treatment plants 6A to 6Nis predicted using a corresponding case base 54. The inflow predictionsection 73 acquires the prediction condition 40 necessary for predictingthe sewage inflow through the communication network 9. For example, themeteorological parameter is successively acquired from themeteorological data providing center 8 connected through thecommunication network 9. The historical data 30 is acquired from thesewage treatment plants 6A to 6N through the communication network 9.

The data broadcast section 74 broadcasts the prediction data to thesewage treatment plants 6A to 6N through the communication network 9. Atthis time, the registrant is authenticated on the basis of the user IDand password. In addition, the historical data 30 from the sewagetreatment plants 6A to 6N are received and transferred to the inflowprediction section 73.

The prediction data broadcast server 7 having the management section 71,model generation section 72, inflow prediction section 73, and databroadcast section 74 is formed from at least one server apparatus formedfrom a computer.

[Operation of Sewage Inflow Prediction Data Broadcast System]

The operation of the sewage inflow prediction data broadcast system willbe described next with reference to FIG. 3. FIG. 3 is a sequence chartshowing an operation example of the sewage inflow prediction databroadcast system. In the following description, assume that the sewagetreatment plant 6A is registered as a registrant by the managementsection 71 in advance, and the prediction model for the sewage treatmentplant 6A is also generated by the model generation section 72 inadvance.

First, the sewage treatment plant 6A is connected to the data broadcastsection 74 of the prediction data broadcast server 7 through thecommunication network 9 at a period of, e.g., about 20 min, as needed,to log in using the user ID and password issued at the time of databroadcast contract (step 90). The data broadcast section 74 checks theauthenticity using the user ID and password (step 91). When it isconfirmed that the sewage treatment plant 6A is an authentic registrant,the data broadcast section 74 sends an authentication OK notification tothe sewage treatment plant 6A through the communication network 9 (step92). Accordingly, the sewage treatment plant 6A requests broadcast ofinflow prediction data and uploads the inflow parameter 41 to be usedfor sewage inflow prediction to the data broadcast section 74 throughthe communication network 9 (step 93).

In accordance with the prediction data broadcast request, the databroadcast section 74 transfers the received inflow parameter 41 to theinflow prediction section 73 together with the request to requestprediction of the inflow prediction data (step 94). In accordance withthe request, the inflow prediction section 73 requests themeteorological data providing center 8 through the communication network9 to provide the meteorological parameter 43, and meteorological dataherein, to be used for prediction (step 95).

The meteorological data providing center 8 broadcasts correspondingmeteorological data to the prediction data broadcast server 7 throughthe communication network 9 in accordance with the providing request(step 96). The inflow prediction section 73 predicts the desired inflowprediction data 20 from the prediction model of the sewage treatmentplant 6A under the prediction condition 40 including the inflowparameter 41 from the sewage treatment plant 6A, which is received fromthe data broadcast section 74, the meteorological data (meteorologicalparameter 43) acquired from the meteorological data providing center 8,and the time parameter 42 obtained from calendar information managed bythe inflow prediction section 73 (step 97) and transfers the inflowprediction data 20 to the data broadcast section 74 (step 98).

The data broadcast section 74 broadcasts the inflow prediction data 20from the inflow prediction section 73 to the sewage treatment plant 6Athrough the communication network 9 (step 99). The sewage treatmentplant 6A receives the inflow prediction data 20. On the basis of thecontents, an appropriate measure is taken. The prediction data broadcastserver 7 executes the series of prediction data broadcast processesrelated to the sewage treatment plant 6A for each of the sewagetreatment plants 6A to 6N.

In this way, the prediction data broadcast server 7 predicts the sewageinflow for each of the sewage treatment plants 6A to 6N using theprediction model for each of the sewage treatment plants 6A to 6N andbroadcasts the prediction data to the sewage treatment plants 6A to 6Nthrough the communication network 9 such as the Internet. For thisreason, the apparatus necessary for predicting the sewage inflow neednot be arranged for each of the sewage treatment plants 6A to 6N, andthe facility cost can be largely reduced. In addition, the personnelexpenses for prediction model generation or inflow prediction can bereduced.

The prediction data broadcast server 7 automatically acquires themeteorological parameter (meteorological data) necessary for predictionfrom the meteorological data providing center 8 through thecommunication network 9. The sewage treatment plants 6A to 6N need tosend only the inflow parameter measured at the sewage treatment plantand can acquire useful inflow prediction data at a very small operationload.

[Case Base]

The operation of the sewage inflow prediction apparatus using the casebase will be described next in detail. First, the operation of the casebase generation section 53 of the sewage inflow prediction apparatus 50will be described with reference to FIGS. 4, 5A, 5B, 5C, 5D, and 6. FIG.4 is an explanatory view showing the concept of phases used in acase-based reasoning model. FIGS. 5A, 5B, 5C, and 5D are explanatoryviews showing quantization processing of an input space. FIG. 6 is aflow chart showing case base generation processing.

In the case-based reasoning model of this embodiment, an input space isquantized into a topological space on the basis of the concept ofconsecutive mapping in the topology of mathematics whereby the case baseand similarity corresponding to an output allowable error (requiredaccuracy) are generally defined.

The concept of consecutive mapping in the topology means that in, e.g.,spaces X and Y, a necessary and sufficient condition for a consecutivemapping f: X→Y is that a reverse mapping f-1 (O) of an open set (outputneighborhood) O in Y corresponds to an open set (input neighborhood) inX. Using the concept of the consecutive mapping, on the basis of apremise that the mapping f from the input space to the output spacecontinues, an output neighborhood is defined using the allowable widthof output error in the output space, as shown in FIG. 4. With thisoperation, the output neighborhoods can be made to correspond to inputneighborhoods that satisfy the allowable widths of output errors so thatthe input space can be quantized and regarded as a topological space.

[Quantization of Input Space]

In this embodiment, input space quantization processing is executed asshown in FIG. 5. Historical data is formed from a combination of inputdata and output data obtained in the past. As shown in FIG. 5A,historical data is formed from inputs x1 and x2 and output y. Thesehistorical data are distributed in the input space x1-x2, as shown inFIG. 5B. When the historical data are quantized on meshes having anequal pitch and predetermined widths in the x1 and x2 directions, asshown in FIG. 5C, the size of each mesh, i.e., input quantization numberis determined in consideration of an allowable width ε of output error,as shown in FIG. 5D.

The allowable width ε of output error is a value representing the degreeof allowance of the error between the output obtained by estimation andan unknown true value for newly input data and is set in advance as amodeling condition. When the size of each mesh is determined using theallowable width ε, an input neighborhood corresponding to the size of anoutput neighborhood, i.e., the case can be defined. The errors of outputdata estimated from all input data belonging to the case satisfy theallowable width ε of output error.

The case base generation section 53 generates the case base 54 usingsuch input space quantization processing. Referring to FIG. 6, first,the historical data 52 is loaded (step 100). A modeling condition suchas the allowable width ε of output error is set (step 101). Variousevaluation indices are calculated on the basis of the allowable width ε,and the input quantization number is selected for each input variable onthe basis of the evaluation indices (step 102). Each case thatconstructs the case base 54 is generated from the historical data 52distributed to each mesh (step 103).

Determination processing of the input quantization number using theevaluation indices will be described now with reference to FIGS. 7, 8,9, and 10. FIG. 7 is a flow chart showing input quantization numberdetermination processing. FIG. 8 is an explanatory view showing anoutput distribution condition as one of the evaluation indices. FIG. 9is an explanatory view showing a continuity condition as one of theevaluation indices. FIG. 10 is an explanatory view showing a casecompression condition as one of the evaluation indices.

In the determination processing of the input quantization number, first,an evaluation criterion (threshold value) is set as a criterion to beused to determine whether the evaluation indices are appropriate (step110). Each evaluation index is calculated for each input quantizationnumber (step 111). The obtained evaluation index is compared with theevaluation criterion, and one of the input quantization numbers forwhich evaluation indices that satisfy the evaluation criterion isselected (step 112). As the evaluation criterion, an input quantizationnumber for which both the output distribution condition and thecontinuity condition are satisfied at 90% or more is preferablyselected. In the system, a divide number of 90% or 95% is indicated. Thevalue “90%” or “95%” can statistically be regarded as an appropriatevalue.

The output distribution condition is a condition that for an arbitrarymesh obtained by quantizing the input space by the selected inputquantization number, the output distribution width of the output y ofthe historical data belonging to the mesh is smaller than the allowablewidth ε of the output error, as shown in FIG. 8. It is thus checkedwhether one mesh, i.e., input neighborhood satisfies the conditiondefined for a corresponding output neighborhood, i.e., the allowablewidth ε of the output error.

The continuity condition is a condition that for an arbitrary meshobtained by quantizing the input space by the selected inputquantization number, the difference between the output value y of a casegenerated in the mesh and an average output value y′ of peripheral casesthat are present around the case is smaller than the allowable width εof the output error, as shown in FIG. 9. It is thus checked whether thedifference in output value between the cases, i.e., between the inputneighborhoods satisfies the condition defined for a corresponding outputneighborhood, i.e., the allowable width ε of the output error. When thecontinuity condition is satisfied, it can be determined that the inputspace is covered by the cases that continuously satisfy a desiredaccuracy.

The case compression condition is a condition of the compression ratioof historical data by case generation. As shown in FIG. 10, when aplurality of historical data belong to an arbitrary mesh obtained byquantizing the input space by the selected input quantization number, itmeans that the plurality of k historical data are compressed to 1/k andconverted into one data representing the cases by case generation of thehistorical data. It is checked herein whether the case compression ratioof the entire historical data satisfies the allowable compression ratiodesignated as a modeling condition.

The input quantization number is sequentially determined for each inputvariable. For example, when input variables are x1, x2, . . . , xn,input quantization numbers are determined sequentially for x1 to xn. Tocalculate an evaluation index, input quantization numbers must beassigned to all input variables. Hence, to obtain an evaluation indexfor xi, an input quantization number that has already been determined atthat time is used for x1 to xi−1, and the same input quantization numberas that for xi is used for xi+1, . . . , xn after xi.

For the output distribution condition and continuity condition of theabove-described conditions, the ratio of cases that satisfy theconditions to all the cases, i.e., an evaluation index sufficiency ratiois used as an evaluation index. For example, the evaluation index valueof an input quantization number m for xi is obtained by quantizing eachof the input range widths of x1, x2, . . . , xn by a corresponding inputquantization number and obtaining the ratio of cases that satisfy theevaluation index condition to all the cases generated by quantization.

For the case compression condition, the case compression ratio of theentire historical data, which is obtained by quantizing each of theinput range widths of all the input variables x1, x2, . . . , xn by thecorresponding input quantization number, is used as the evaluation indexvalue for the input quantization number m for xi.

For the input variable xi, one of the input quantization numbers forwhich all the evaluation index values satisfy the evaluation criterionis selected and determined as the input quantization number for theinput variable xi.

[Generation of Case Base]

The case base generation section 53 selects the input quantizationnumber in the above-described way and distributes the historical data tothe input space, i.e., each mesh quantized by the input quantizationnumber to generate cases. FIGS. 11A, 11B, 11C, and 11D are explanatoryviews showing case generation processing. FIG. 12 is a flow chartshowing case generation processing.

First, each input variable is quantized (partitioned) on the basis ofthe selected input quantization number to generate a mesh (step 120).Referring to FIG. 11A, the input variable x1 is divided into 10 pieces,and the input variable x2 is divided into 6 pieces.

The historical data are distributed to the meshes (step 121). A meshhaving historical data is selected as a case, and its input and outputvalues are calculated (step 122). As shown in FIG. 11B, when threehistorical data are distributed to one mesh, these historical data areintegrated into one case (FIG. 11C). At this time, the average value ofthe outputs y of the thee historical data is used as an output valuethat represents the case. The median of the mesh is used as an inputvalue that represents the case (FIG. 11D).

[Estimation of Sewage Inflow]

The sewage inflow prediction apparatus 50 shown in FIG. 1 estimates thesewage inflow from the newly input prediction condition 40 using thecase base 54 generated in the above-described way.

First, in the similar case search section 56, the input section 51samples the prediction condition 40 to generate input variables andsearches the case base 54 for a similar case using the similarity. FIG.13 is an explanatory view showing the definition of similarity. FIG. 14is a flow chart showing similar case search processing in the similarcase search section 56.

The similarity is an index that represents the degree of similaritybetween each case in each mesh formed in the input space of the casebase 54 and a mesh corresponding to the new prediction condition, i.e.,input data. Referring to FIG. 13, when a case is present in the centralmesh corresponding to input data, the case and input data have“similarity=0”. A case that is present in a mesh immediately adjacent tothe central mesh has “similarity=1”. As the case separates from thecentral mesh by one mesh, the similarity increases by one.

Hence, when estimation is performed, the estimated value by a case witha similarity i has an accuracy within (i+1)×output allowable width. Atthis time, when the cases on both sides are appropriately used for theinput value subjected to estimation, the output value is expected tohave a higher accuracy than (i+1)×output allowable width. When only thecase on one side is used for the value subjected to estimation, it isexpected that the accuracy is as high as (i+1)×output allowable widthbecause of the continuity of input/output.

As shown in FIG. 14, first, the similar case search section 56 receivesthe new prediction condition sampled by the input section 51 as inputdata (step 130), selects a mesh corresponding to the input data from theinput space of the case base 54 (step 131), initializes the similarityto be used as a case search range to 0 (step 132), and searches the casesearch range indicated by the similarity for a similar case (step 133).

When a case is present in the mesh corresponding to the input data (step134: YES), the case is output as a similar case (step 136).

On the other hand, if no case is present in the mesh corresponding tothe input data (step 134: NO), the similarity is incremented by one towiden the case search range (step 135). Then, the flow returns to step133 to search for a similar case again.

In this way, the similar case search section 56 searches the case base54 for a similar case corresponding to the new prediction condition. Theoutput estimation section 57 estimates the sewage inflow correspondingto the new prediction condition on the basis of the similar case.

FIGS. 15A and 15B are explanatory views showing output estimationoperation (when a similar case is present). For example, when a case ispresent in a mesh 150 corresponding to input data A (22.1,58.4) (FIG.15A), the output value y=70.2 of the case is selected as the estimatedoutput value (FIG. 15B).

FIGS. 16A and 16B are explanatory views showing output estimationoperation (when no similar case is present). When no case is present ina mesh 151 corresponding to the input data A (23.8,62.3), a search range152 is widened, and a similar case is searched for (FIG. 16A). Anestimated output value is calculated from the detected case. At thistime, when a plurality of cases are detected, the average value of theoutput values of the cases is used as the estimated output value (FIG.16B).

The sewage inflow corresponding to the new prediction condition 40 isestimated in this way. The inflow prediction data 20 based on theestimated amount is instructed from the output estimation section 57 tothe sewage treatment plant 10.

[Adaptive Learning]

The operation of the adaptive learning section will be described next.

The adaptive learning section 55 updates the case base 54 on the basisof the new historical data 30 obtained from the input section 51. Thehistorical data 30 may automatically be obtained every, e.g., 1 hr usinga calendar function or temperature sensor such that automatic operationcan be performed.

First, a case corresponding to new data is searched for from the inputspace of the case base 54. When a case corresponding to the new data ispresent, only the case is revised.

FIGS. 17A and 17B are explanatory views showing adaptive learningoperation when a corresponding case is present. In this situation, sincea case 160 corresponding to new data B (23.9,66.8,48.2) is present (FIG.17A) the new output value y=49.0 of the case is output from the outputvalue y=48.2 of the new data B and the output value=49.7 of the case 160before revision (FIG. 17B). As an output revision expression, forgettingcoefficient C_(Forget) is used. An output value Y_(old) 5 beforerevision and an output value Y of the new data B are added at the ratioindicated by the forgetting coefficient C_(Forget), and an output valueY_(new) of the case after revision is obtained. The output revisionexpression is given byY _(new)(1.0−C _(Forget))×Y _(old) +C _(Forget) ×Y

On the other hand, when no case corresponding to the new data ispresent, a new case is generated on the basis of the new data.

FIGS. 18A and 18B are explanatory views 15 showing adaptive learningoperation when no corresponding case is present. In this situation,since no case is present in a mesh 161 corresponding to the new data B(23.7,62.3,43.8) (FIG. 18A), the median of the mesh corresponding to thenew data B is used as an input 20 value. A new case 162 (23.8,62.5,43.8)whose representative output value is the output value y of the new dataB is newly generated and added to the case base 54 (FIG. 18B).

FIG. 19 shows a simulation result representing 25 a predicted value 170and actual value 171 of the sewage inflow and a rainfall (1-hr rainfall)172 when a case-based reasoning model is used. In this situation, assumethat the sewage inflow at the time T1 90 min after the prediction timeT0 is predicted. As the prediction condition 40, the air temperature atthe time T0, the type of day at the time T1, and the time inflow for thepast 1 hr, which is obtained 90 min (in this situation, at the time T0)before the time T1, are used.

As shown in FIG. 19, the obtained predicted value 170 is almost the sameas the actual value 171 of the sewage inflow measured at the sewagetreatment plant 10. Even when the sewage inflow changes over time, andthe rainfall 172 largely changes, the predicted value 170 follows upwithout any delay.

[Comparison Between Case-based reasoning Model and Conventional ModelingTechnique]

In the reasoning model used in the sewage inflow prediction apparatus50, the concept of the case-based reasoning is applied to modeling. Thiscan be said to be a modeling technique which can be applied to a generalobject for which the system input/output relationship maintainscontinuity on the basis of the concept of topology. In a generalmodeling technique, a model parameter such as the model order or networkstructure is identified. In the case-based reasoning model of thisembodiment, however, a desired output allowable error is designated toidentify the topology of the input space.

Hence, as a characteristic feature, data is stored in the specifiedinput space as a case, and at the time of output estimation, thereliability of the estimated output value can be represented by thetopological distance (similarity) between an input and the input casestored in advance. In this embodiment, since the sewage inflow in futureis estimated using such a model, the following functions/effects can beobtained as compared to a neural network or nonlinear regression model.

In a neural network or nonlinear regression model:

1) Since a special model structure is used to specify the relationshipin the entire input/output region, a long time is required to find astructure optimum for the system.

2) To learn a large quantity of historical data, convergence calculationmust be performed to identify the plurality of parameters of the modelstructure. This also takes a long time.

3) Even when a model is updated on the basis of new data, the parametersmust be identified, and therefore, adaptive learning is difficult infact.

4) It is difficult to grasp the degree of reliability of the modeloutput value for the input value to be estimated.

To the contrary, according to the above embodiment:

1) Since cases (questions and answers) that are experienced in the pastare stored as the case base, and input/output cases that involve theinput/output relationship of the system are used, no special model forrepresenting the input/output relationship is necessary.

2) For a newly input question, an existing case having a similarquestion is searched for from the case base. At this time, the inputspace is quantized using an input quantization number as a parameter todefine the case base and similarity. The quantization number isdetermined by calculating an evaluation index value. For this reason, noconvergence calculation is necessary. In addition, the degree ofcompletion of the model can be evaluated on the basis of the evaluationindex value. Unlike the conventional technique, the model need not beevaluated using independently test data.

Also, according to the above embodiment:

3) The answer of a detected similar case is corrected to obtain theanswer for the newly input question. Since the degree of similarity ofthe case detected for the input value to be estimated can be determined,the similarity can be used to evaluate the reliability of the outputvalue.

4) After the correct answer for the newly input question is obtained,the new case is added to the case base. Hence, the case base can bepartially revised on the basis of the new data. Parameter identificationas in the conventional technique is unnecessary, and adaptive learningcan easily be executed.

The problem of learning and convergence calculation in the conventionalmodel is equivalent to the problem of definition of the case-basedstructure and similarity in the case-based reasoning (CBR). In theconventional case-based reasoning, it is a serious engineering problem;definition is impossible unless sufficient information of an object ispresent. In the case-based reasoning model of the above embodiment, thefirst definition of the case base and similarity according to the outputallowable error, i.e., requested accuracy is done on the basis of theconcept of consecutive mapping in the topology of mathematics byquantizing the input space into a topological space.

As has been described above, according to the sewage inflow predictionapparatus of the above embodiment, a prediction model is generated fromhistorical data containing inflow data that indicates the sewage inflowmeasured at the sewage treatment facility and meteorological datacorresponding to the inflow data. The sewage inflow corresponding to aninput prediction condition is predicted in real time. For this reason,the sewage inflow prediction apparatus has a practical operability andcan predict the inflow in future at a sufficient reliability inconsideration of the time required for sewage purification treatment.Hence, as compared to the prior art that predicts using formulas basedon physical laws, prediction can be executed using a relatively smallnumber of variables. Rough city design information need not be used.

In addition, in the sewage inflow prediction apparatus and serverapparatus, a prediction data broadcast server connected to each sewagetreatment facility through a communication network receives an inflowparameter that indicates a sewage inflow newly measured at any one ofthe sewage treatment facilities from the sewage treatment facilitythrough the communication network, acquires meteorological datacorresponding to the inflow parameter, through the communication networkas a meteorological parameter, from a meteorological data providingcenter that provides meteorological data, predicts the sewage inflowcorresponding to a prediction condition including the inflow parameterand meteorological parameter in real time using the prediction model ofthe sewage treatment plant, and broadcasts inflow prediction dataobtained by prediction to the sewage treatment facility through thecommunication network.

Hence, in addition to the above effects, the apparatus necessary forpredicting the sewage inflow need not be arranged for each sewagetreatment facility, and the facility cost can be largely reduced.Furthermore, the personnel expenses for prediction model generation orinflow prediction can be reduced.

Also, the prediction data broadcast server automatically acquires themeteorological parameter (meteorological data) necessary for predictionfrom the meteorological data providing center through the communicationnetwork. The sewage treatment facility side needs to send only theinflow parameter measured at the sewage treatment plant and can acquireuseful inflow prediction data at a very small operation load.

1. A sewage inflow prediction apparatus which provides sewage inflowprediction for a sewage treatment facility comprising: a case basegenerated from historical data including inflow data that indicates asewage inflow previously measured at the sewage treatment facility andmeteorological data corresponding to the inflow data; and predictionmeans for detecting a substantially similar case corresponding to aninput prediction condition from said case base and predicting a sewageinflow on the basis of the detecting result in real time, wherein firstinflow data that indicates a sewage inflow measured at a first time andsecond inflow data that indicates a sewage inflow measured after apredetermined prediction time from the first inflow data are used as theinflow data, meteorological data corresponding to the second inflow datais used as the meteorological data, an inflow parameter that indicates asewage inflow newly measured at the sewage treatment facility and ameteorological parameter obtained by meteorological prediction are usedas a prediction condition, said case base has a plurality of inputspaces, which are quantized in accordance with a desired outputallowable error, the first inflow data and meteorological data includedin historical data and at least one representative case is provided foreach of the input spaces, the case base has an output value thatrepresents at least one historical data arranged in at least one spaceof the plurality of input spaces on the basis of an input variablevalue, said prediction means searches the case base for detecting asubstantially similar case corresponding to a new prediction conditionas an input variable value and estimates inflow prediction datacorresponding to the new prediction condition using an output value ofthe detected substantially similar case, the sewage inflow prediction isnot created in advance based on physical laws, the sewage inflowprediction is used by the sewage treatment facility to control sewagepurification treatment, and when a plurality of historical data aredistributed on one input space of the plurality of input spaces, anaverage value of outputs of the plurality of historical data is used asan output value of the at least one representative case and a median ofone input space is used as an input value of the at least onerepresentative case.
 2. The sewage inflow prediction apparatus accordingto claim 1, wherein air temperature and rainfall in an objective regionof the sewage treatment facility are used as the meteorological data. 3.The sewage inflow prediction apparatus according to claim 1, furthercomprising: adaptive learning means for modifying the case base, saidadaptive learning means using as input a combination of third inflowdata that indicates a sewage inflow newly measured at the sewagetreatment facility, fourth inflow data that indicates a sewage inflowmeasured before a predetermined prediction time for the third inflowdata, and meteorological data corresponding to the third inflow data,wherein said adaptive learning means modifies an output value of apredetermined case of a case base corresponding to the fourth inflowdata and meteorological data on the basis of the third inflow data.